## [1] "Loading the following libraries using lb_myRequiredPackages: data.table"
## [2] "Loading the following libraries using lb_myRequiredPackages: lubridate"
## [3] "Loading the following libraries using lb_myRequiredPackages: ggplot2"
## [4] "Loading the following libraries using lb_myRequiredPackages: readr"
## [5] "Loading the following libraries using lb_myRequiredPackages: plotly"
## [6] "Loading the following libraries using lb_myRequiredPackages: knitr"1 Purpose
To extract and visualise tweets and re-tweets of #dockercon for 17 - 21 April, 2017 (DockerCon17).
Borrowing extensively from http://thinktostart.com/twitter-authentification-with-r/
2 Load Data
Data should have been already downloaded using collectData.R. This produces a data table with the following variables (after some processing):
## [1] "text" "favorited" "favoriteCount"
## [4] "replyToSN" "created" "truncated"
## [7] "replyToSID" "id" "replyToUID"
## [10] "statusSource" "screenName" "retweetCount"
## [13] "isRetweet" "retweeted" "longitude"
## [16] "latitude" "location" "language"
## [19] "profileImageURL" "createdLocal" "obsDateTimeMins"
## [22] "obsDateTimeHours" "obsDateTime5m" "obsDateTime10m"
## [25] "obsDateTime15m" "obsDate" "isRetweetLab"The table has 7,525 tweets (and 10,308 re-tweets) from 5,962 tweeters between 2017-04-16 19:01:03 and 2017-04-20 09:13:22 (Central District Time).
3 Analysis
3.1 Tweets and Tweeters over time
All (re)tweets containing #dockercon 2017-04-17 to 2017-04-20
3.1.1 Day 1 - Monday (Workshops)
All (re)tweets containing #dockercon Monday 17th April 2017
3.1.2 Day 2 - Tuesday (Main Day 1)
All (re)tweets containing #dockercon Tuesday 18th April 2017
3.1.3 Day 3 - Wednesday (Main Day 2)
All (re)tweets containing #dockercon Wednesday 19th April 2017
3.1.4 Day 4 - Thursday (Main Day 3)
All (re)tweets containing #dockercon Thursday 20th April 2017
3.2 Location (lat/long)
We wanted to make a nice map but sadly we see that most tweets have no lat/long set.
| latitude | longitude | nTweets |
|---|---|---|
| NA | NA | 17781 |
| 30.26416397 | -97.73961067 | 2 |
| 30.26857 | -97.73617 | 1 |
| 30.2625 | -97.7401 | 29 |
| 30.26470908 | -97.7417368 | 1 |
| 30.20226566 | -97.66722505 | 1 |
| 42.36488267 | -71.02168356 | 1 |
| 37.61697678 | -122.38427689 | 1 |
| 30.2672 | -97.7639 | 3 |
| 30.2635554 | -97.7399303 | 1 |
| 30.2591 | -97.7384 | 1 |
| 30.26622515 | -97.74327721 | 1 |
| 30.26037 | -97.73848 | 3 |
| 30.258201 | -97.71264 | 1 |
| 30.25888 | -97.73841 | 2 |
| 30.259714 | -97.73940054 | 1 |
| 30.26006 | -97.73813 | 1 |
| 30.26006 | -97.73859 | 1 |
| 30.26036009 | -97.73848483 | 1 |
3.3 Location (textual)
This appears to be pulled from the user’s profile although it may also be a ‘guestimate’ of current location.
Top locations for tweets:
| location | nTweets |
|---|---|
| NA | 2727 |
| San Francisco, CA | 1277 |
| San Francisco | 499 |
| Austin, TX | 329 |
| Seattle, WA | 226 |
| Silicon Valley, CA | 194 |
| Paris | 179 |
| Islamabad, Pakistan | 140 |
| London | 133 |
| New York, NY | 122 |
| Charlotte, NC | 119 |
| San Jose, CA | 114 |
| Boston, MA | 106 |
| west tokyo | 104 |
| Boulder, CO | 103 |
Top locations for tweeters:
| location | nTweeters |
|---|---|
| NA | 1106 |
| San Francisco, CA | 175 |
| Austin, TX | 86 |
| San Francisco | 61 |
| Seattle, WA | 49 |
| New York, NY | 41 |
| Paris | 41 |
| San Jose, CA | 40 |
| London, England | 34 |
| Paris, France | 33 |
| London | 31 |
| Palo Alto, CA | 29 |
| New York | 28 |
| Boston, MA | 27 |
| France | 27 |
3.4 Screen name
Next we’ll try by screen name.
Top tweeters:
| screenName | nTweets |
|---|---|
| DockerCon | 325 |
| theCUBE | 156 |
| climbingkujira | 127 |
| jpetazzo | 124 |
| BettyJunod | 123 |
| solomonstre | 111 |
| jeanepaul | 104 |
| ManoMarks | 91 |
| OpenShiftNinja | 87 |
| sitspak | 80 |
| kaslinfields | 80 |
| vmblog | 78 |
| SFoskett | 78 |
| jameskobielus | 76 |
| bsmith626 | 73 |
And here’s a really bad visualisation of all of them!
N tweets per 5 minutes by screen name
So let’s re-do that for the top 50 tweeters.
N tweets per 5 minutes by screen name (top 50, most prolific tweeters at bottom)
4 About
Analysis completed in: 41.08 seconds using knitr in RStudio with R version 3.3.3 (2017-03-06) running on x86_64-apple-darwin13.4.0.
A special mention must go to twitteR (Gentry, n.d.) for the twitter API interaction functions and lubridate (Grolemund and Wickham 2011) which allows timezone manipulation without tears.
Other R packages used:
- base R - for the basics (R Core Team 2016)
- data.table - for fast (big) data handling (Dowle et al. 2015)
- readr - for nice data loading (Wickham, Hester, and Francois 2016)
- ggplot2 - for slick graphs (Wickham 2009)
- plotly - fancy, zoomable slick graphs (Sievert et al. 2016)
- knitr - to create this document (Xie 2016)
References
Dowle, M, A Srinivasan, T Short, S Lianoglou with contributions from R Saporta, and E Antonyan. 2015. Data.table: Extension of Data.frame. https://CRAN.R-project.org/package=data.table.
Gentry, Jeff. n.d. TwitteR: R Based Twitter Client. http://lists.hexdump.org/listinfo.cgi/twitter-users-hexdump.org.
Grolemund, Garrett, and Hadley Wickham. 2011. “Dates and Times Made Easy with lubridate.” Journal of Statistical Software 40 (3): 1–25. http://www.jstatsoft.org/v40/i03/.
R Core Team. 2016. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Sievert, Carson, Chris Parmer, Toby Hocking, Scott Chamberlain, Karthik Ram, Marianne Corvellec, and Pedro Despouy. 2016. Plotly: Create Interactive Web Graphics via ’Plotly.js’. https://CRAN.R-project.org/package=plotly.
Wickham, Hadley. 2009. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. http://ggplot2.org.
Wickham, Hadley, Jim Hester, and Romain Francois. 2016. Readr: Read Tabular Data. https://CRAN.R-project.org/package=readr.
Xie, Yihui. 2016. Knitr: A General-Purpose Package for Dynamic Report Generation in R. https://CRAN.R-project.org/package=knitr.